Geometric basis-vector selection methods and subpixel target detection as applied to hyperspectral imagery

TLDR: This paper compares three basis-vector selection methods as applied to subpixel target detection and finds ROC curves to describe the relationship between the detection rate (DR) and the false alarm rate (FAR).

Abstract: In this paper, we compare three basis-vector selection methods as applied to subpixel target detection. This is a continuation of previous research in which a similar comparison was performed based on an AVIRIS image. Our goal is to find out to what extent our previous observations apply more broadly to other images, more specifically, a HYDICE image used in this paper. Our target detection approach is based on generating a radiance target region using a physical model to generate radiance spectra as observed under a wide range of atmospheric, illumination., and viewing conditions. The advantage of this approach is that the resulting target detection is invariant to those changing conditions. For the purpose of target detection, we use a structured model to describe each image spectra as a linear combination of the target and background basis-vectors, and then we apply a matched subspace detector. Finally, we find ROC curves to describe the relationship between the detection rate (DR) and the false alarm rate (FAR). Due to a large number of cases considered, we use summary metrics to represent our results. The obtained results are quite different from those obtained in (Bajorski et al., 2004) for the AVIRIS image. The best method for generating the background basis vectors in the AVIRIS image was the MaxD method, while the SVD method proved to be best for the HYDICE image used in this paper. Further research is needed to find out the reasons for these differences. It is not surprising that different methods are optimal for different types of data. However, it would be useful to be able to recognize the optimal method without assuming knowledge of the targets in the image